Combining VAR and DSGE forecast densities

A popular macroeconomic forecasting strategy takes combinations across many models to hedge against model instabilities of unknown timing; see (among oth-ers) Stock & Watson (2004), Clark & McCracken (2009), and Jore, Mitchell and Vahey (2009). The scope of such ensemble forecasting exercises usually excludes Dynamic Stochastic General Equilibrium (DSGE) models, such as those advocated by Del Negro and Schorfheide (2004) and Smets and Wouters (2007), limiting the computational burden. In this paper, we use an expert combination framework (Winkler, 1981) to combine forecast densities from Vector Autoregressions (VARs), and a DSGE model (NEMO: the Norges Bank core policymaking macromodel). We show that the predictive densities from the DSGE model are competitive with those from a VAR ensemble if the VAR components are restricted to have constant pa-rameters. In this case, both the VAR ensemble and the DSGE forecast densities are poorly calibrated. However, a VAR ensemble which encompasses structural break components produces well-calibrated forecast densities. The VARs with breaks are relatively responsive to structural breaks of unknown timing